Abstract
Inspired by the recent successes of deep learning on Computer Vision, we propose a deep learning-based system for Automatic License Plate Recognition (ALPR). The recognition system has two main modules: license plate detection (LPD) and license plate recognition (LPR). We employ anchor clustering, generalized IoU, and focal loss for improving YOLO based license plate detection and a method to generate synthesis license places to improve character recognition. The experiments on UFPR-ALPR and VN-ALPR datasets show that our recognition system achieved 94.06% and 96.00% for the ALPR task, respectively. Moreover, our recognition system achieves real-time processing at 30–32 FPS.
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References
Elihos, A., Balci, B., Alkan, B., Artan, Y.: Deep learning based segmentation free license plate recognition using roadway surveillance camera images. arXiv:1912.02441 (2019)
Lee, Y., Lee, J., Ahn, H., Jeon, M.: SNIDER: single noisy image denoising and rectification for improving license plate recognition. In: 2019 IEEE International Conference on Computer Vision (ICCV) Workshop (2019)
Yang, X., Wang, X.: Recognizing license plates in real-time. arXiv:1906.04376 (2019)
Laroca, R., Zanlorensi, L.A., Goncalves, G.R., Todt, E., Schwartz, W.R., Menotti, D.: An efficient and layout-independent automatic license plate recognition system based on the YOLO detector. arXiv:1909.01754 (2019)
OpenALPR. https://www.openalpr.com/
Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: unified, real-time object detection. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 779–788 (2016)
Le, A.D., Nguyen, H.T., Nakagawa, M.: An end-to-end recognition system for unconstrained Vietnamese handwriting. SN Comput. Sci. 1, 7 (2020). https://doi.org/10.1007/s42979-019-0001-4
Acknowledgment
We thank Dr. Dung Duc Nguyen, Institute of Information Technology, Vietnam and Rayson Laroca, Vision, Robotics, and Imaging Research Group, the Federal University of Parana for providing the VN-ALPR and UFPR-ALPR datasets, respectively.
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© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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Le, A., Pham, D., Lam, T. (2023). Robust and Accurate Automatic License Plate Recognition System. In: Dang, T.K., KĂĽng, J., Chung, T.M. (eds) Future Data and Security Engineering. Big Data, Security and Privacy, Smart City and Industry 4.0 Applications. FDSE 2023. Communications in Computer and Information Science, vol 1925. Springer, Singapore. https://doi.org/10.1007/978-981-99-8296-7_44
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DOI: https://doi.org/10.1007/978-981-99-8296-7_44
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